class_docstr: Handle training of custom HuggingFace code.
class_docstr_tok: ["Handle","training","of","custom","HuggingFace","code","."]
docstr: Placeholder docstring
docstr_tok: ["Placeholder","docstring"]
code: def _validate_args(self, image_uri):
      """Placeholder docstring"""
      if image_uri is not None:
          return
  
      if self.framework_version is None and image_uri is None:
          raise ValueError(
              "transformers_version, and image_uri are both None. "
              "Specify either transformers_version or image_uri"
          )
      if (
          self.tensorflow_version is not None
          and self.pytorch_version is not None
      ):
          raise ValueError(
              "tensorflow_version and pytorch_version are both not None. "
              "Specify only tensorflow_version or pytorch_version."
          )
      if (
          self.tensorflow_version is None
          and self.pytorch_version is None
      ):
          raise ValueError(
              "tensorflow_version and pytorch_version are both None. "
              "Specify either tensorflow_version or pytorch_version."
          )
      base_framework_version_len = (
          len(self.tensorflow_version.split("."))
          if self.tensorflow_version is not None
          else len(self.pytorch_version.split("."))
      )
      transformers_version_len = len(self.framework_version.split("."))
      if transformers_version_len != base_framework_version_len:
          raise ValueError(
              "Please use either full version or shortened version for both "
              "transformers_version, tensorflow_version and pytorch_version."
          )
  
file_path: sagemaker-python-sdk/src/sagemaker/huggingface/estimator.py
imports: from __future__ import absolute_import
  import logging
  import re
  from typing import Optional, Union, Dict
  from sagemaker.estimator import Framework, EstimatorBase
  from sagemaker.fw_utils import framework_name_from_image, validate_distribution
  from sagemaker.huggingface.model import HuggingFaceModel
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.huggingface.training_compiler.config import TrainingCompilerConfig
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 2 for calling the HuggingFace obj's underlying member method code with example usage: obj._validate_args(image_uri) without return types